Qwen1.5-MoE-A2.7B is a large-scale MoE (Mixture of Experts) language model with only 2.7 billion activation parameters. Despite its smaller size, it achieves performance comparable to 70 billion parameter models. Compared to traditional large models, this model reduces training costs by 75% and increases inference speed by 1.74 times. It employs a special MoE architecture design, including fine-grained experts, new initialization methods, and routing mechanisms, which significantly enhance model efficiency. This model is suitable for various tasks in natural language processing, code generation, and more.